Artificial intelligence If you look at the issue from the outside; Visual production tools like Stable Diffusion and Midjourney use what we know as a graphics card. GPU and that assistants like ChatGPT use processors, which we know as processors. of CPU You might think it’s getting power. Although this is what is often thought in everyday definitions, GPU leads on both sides, regardless of the situation.

Well, it allows us to easily edit images and videos, play games with solid graphics and create 3D designs. GPUshow come it seems completely irrelevant voice and text assistants Can it give life?

First of all, the problem we need to solve is the distinction between CPU and GPU.

When it comes to CPU in general i3, i7, Ryzen5 Series like this are intended. When it comes to GPUs GTX, RTX, 6600, 7700 There are video card series such as. But to understand this problem, we have to look a little behind the scenes.

Both the CPU and GPU are essentially processing units. CPU The processing unit we know performs successive operations. CPUs that do this quite quickly cannot handle very large data sets, even if they are fast. However, these units, whose main tasks are to ensure the operation of the computer and carry out complex operations, are available in small numbers. to strong nuclei they have.

on the other hand GPU unitsIt houses smaller and more cores than CPUs. These units, produced for graphical operations, also perform parallel calculations. Of course, don’t let this confuse you. We just said that CPUs perform fast operations in sequence. GPUs can perform a large number of operations simultaneously, with the benefit of a higher number of cores. This includes calculations.

Additionally, in the middle of the two sides are APUs, which contain both the CPU and the GPU.

After making this distinction, we come to our main question: why are GPUs preferred over CPUs in artificial intelligence training?

The ability to train artificial intelligence tools depends on how long it takes to process highly complex transactions. To do this as quickly as possible, many complex transactions must be resolved simultaneously. GPUs compared to CPUs more cores We said we had it. This allows these units to conduct many operations simultaneously.

Bandwidth This is also an area where GPUs have an advantage. Thanks to their high bandwidth, these units perform many transactions simultaneously and process the data from these transactions at high speed. An average CPU has a bandwidth of 50 to 100 GB/s, while a high-end GPU can reach more than 500 GB/s.

Moreover, it is designed to make these operations faster. AI-focused GPUs There is also. These models, which have more robust bandwidth and clock speed, are the ones we’ve come to expect. Open GTA 5 Although they don’t look like maps, they make AI learning more efficient. NVIDIAs H100 GPU is one of them.

For those who do not understand what has happened so far, there is a very good example that will clarify this matter.

Let’s imagine that we have to get these people across the street.

This is our CPU:

This is our GPU:

although Our CPU Even though it is already quite advanced, it will take a long time for all these people to get over it. This is obviously not because it is not powerful enough, but because it only has the ability to perform operations sequentially.

on the other hand our GPUBecause it has the capacity to transfer all these people (data) at the same time, we can save both time and costs.

When data is fed into artificial intelligence models, both GPU and CPU play a crucial role. So don’t let thoughts like one happen without the other coming to mind. Because even in areas where the CPU leads, the GPU can remain on the sidelines.